{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train = pd.read_csv(\"/Users/Sinyer/Desktop/kaggle/train.csv\")\n",
    "test = pd.read_csv(\"/Users/Sinyer/Desktop/kaggle/test.csv\")\n",
    "all_X = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'],test.loc[:, 'MSSubClass':'SaleCondition']))\n",
    "\n",
    "numeric_feats = all_X.dtypes[all_X.dtypes != \"object\"].index\n",
    "all_X[numeric_feats] = all_X[numeric_feats].apply(lambda x: (x - x.mean()) / (x.std()))\n",
    "all_X = pd.get_dummies(all_X, dummy_na=True)\n",
    "all_X = all_X.fillna(all_X.mean())\n",
    "\n",
    "num_train = train.shape[0]\n",
    "\n",
    "X_train = all_X[:num_train].as_matrix()\n",
    "X_test = all_X[num_train:].as_matrix()\n",
    "y_train = train.SalePrice.as_matrix()\n",
    "\n",
    "from mxnet import ndarray as nd\n",
    "from mxnet import autograd\n",
    "from mxnet import gluon\n",
    "\n",
    "X_train = nd.array(X_train)\n",
    "y_train = nd.array(y_train)\n",
    "y_train.reshape((num_train, 1))\n",
    "\n",
    "X_test = nd.array(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square_loss = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_rmse_log(net, X_train, y_train):\n",
    "    num_train = X_train.shape[0]\n",
    "    clipped_preds = nd.clip(net(X_train), 1, float('inf'))\n",
    "    return np.sqrt(2 * nd.sum(square_loss(nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)\n",
    "\n",
    "def get_net():\n",
    "    net = gluon.nn.Sequential()\n",
    "    with net.name_scope():\n",
    "        net.add(gluon.nn.Dense(1024,activation='relu'))\n",
    "        net.add(gluon.nn.Dropout(0.5))\n",
    "        net.add(gluon.nn.Dense(1))\n",
    "    net.initialize()\n",
    "    return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(net, X_train, y_train, X_test, y_test, epochs, verbose_epoch, learning_rate, weight_decay):\n",
    "    train_loss = []\n",
    "    if X_test is not None:\n",
    "        test_loss = []\n",
    "    batch_size = 100\n",
    "    dataset_train = gluon.data.ArrayDataset(X_train, y_train)\n",
    "    data_iter_train = gluon.data.DataLoader(\n",
    "        dataset_train, batch_size,shuffle=True)\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': learning_rate, 'wd': weight_decay})\n",
    "    net.collect_params().initialize(force_reinit=True)\n",
    "    for epoch in range(epochs):\n",
    "        for data, label in data_iter_train:\n",
    "            with autograd.record():\n",
    "                output = net(data)\n",
    "                loss = square_loss(output, label)\n",
    "            loss.backward()\n",
    "            trainer.step(batch_size)\n",
    "            cur_train_loss = get_rmse_log(net, X_train, y_train)\n",
    "        if epoch > verbose_epoch:\n",
    "            print(\"Epoch %d, train loss: %f\" % (epoch, cur_train_loss))\n",
    "        train_loss.append(cur_train_loss)\n",
    "        if X_test is not None:    \n",
    "            cur_test_loss = get_rmse_log(net, X_test, y_test)\n",
    "            test_loss.append(cur_test_loss)\n",
    "    plt.plot(train_loss)\n",
    "    plt.legend(['train'])\n",
    "    if X_test is not None:\n",
    "        plt.plot(test_loss)\n",
    "        plt.legend(['train','test'])\n",
    "    plt.show()\n",
    "    if X_test is not None:\n",
    "        return cur_train_loss, cur_test_loss\n",
    "    else:\n",
    "        return cur_train_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, learning_rate, weight_decay):\n",
    "    assert k > 1\n",
    "    fold_size = X_train.shape[0] // k\n",
    "    train_loss_sum = 0.0\n",
    "    test_loss_sum = 0.0\n",
    "    for test_i in range(k):\n",
    "        X_val_test = X_train[test_i * fold_size: (test_i + 1) * fold_size, :]\n",
    "        y_val_test = y_train[test_i * fold_size: (test_i + 1) * fold_size]\n",
    "\n",
    "        val_train_defined = False\n",
    "        for i in range(k):\n",
    "            if i != test_i:\n",
    "                X_cur_fold = X_train[i * fold_size: (i + 1) * fold_size, :]\n",
    "                y_cur_fold = y_train[i * fold_size: (i + 1) * fold_size]\n",
    "                if not val_train_defined:\n",
    "                    X_val_train = X_cur_fold\n",
    "                    y_val_train = y_cur_fold\n",
    "                    val_train_defined = True\n",
    "                else:\n",
    "                    X_val_train = nd.concat(X_val_train, X_cur_fold, dim=0)\n",
    "                    y_val_train = nd.concat(y_val_train, y_cur_fold, dim=0)\n",
    "        net = get_net()\n",
    "        train_loss, test_loss = train(\n",
    "            net, X_val_train, y_val_train, X_val_test, y_val_test, \n",
    "            epochs, verbose_epoch, learning_rate, weight_decay)\n",
    "        train_loss_sum += train_loss\n",
    "        print(\"Test loss: %f\" % test_loss)\n",
    "        test_loss_sum += test_loss\n",
    "    return train_loss_sum / k, test_loss_sum / k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47, train loss: 0.107904\n",
      "Epoch 48, train loss: 0.106057\n",
      "Epoch 49, train loss: 0.107166\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM+sQF29mZmZmHfIf4UbsWFjQJ6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111735ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.119364\n",
      "Epoch 47, train loss: 0.102425\n",
      "Epoch 48, train loss: 0.103100\n",
      "Epoch 49, train loss: 0.103665\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120e8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.143018\n",
      "Epoch 47, train loss: 0.103278\n",
      "Epoch 48, train loss: 0.103177\n",
      "Epoch 49, train loss: 0.102361\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAjAdVT6HGTGRQ+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5af1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.132287\n",
      "Epoch 47, train loss: 0.106505\n",
      "Epoch 48, train loss: 0.106032\n",
      "Epoch 49, train loss: 0.106567\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+QSSx1e6SSTA2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f455710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.113457\n",
      "Epoch 47, train loss: 0.100858\n",
      "Epoch 48, train loss: 0.102223\n",
      "Epoch 49, train loss: 0.103761\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVpD/C0sROSda4Y9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111440160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.151026\n",
      "5-fold validation: Avg train loss: 0.104704, Avg test loss: 0.131830\n"
     ]
    }
   ],
   "source": [
    "k = 5\n",
    "epochs = 50\n",
    "verbose_epoch = 49\n",
    "learning_rate = 0.05\n",
    "weight_decay = 170\n",
    "\n",
    "# train_loss, test_loss = k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, learning_rate, weight_decay)\n",
    "# print(\"%d-fold validation: Avg train loss: %f, Avg test loss: %f\" % (k, train_loss, test_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47, train loss: 0.104336\n",
      "Epoch 48, train loss: 0.103704\n",
      "Epoch 49, train loss: 0.103786\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAGgCAYAAAD8YIB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAASdAAAEnQB3mYfeAAAIABJREFUeJzt3XuUpHV97/v3t6ovM909g8wwjYDI4HAEhTGKIh6vqHFH\nPGQpJm6FuA544sk5YjyuLBM3GtlcDOxsciVGXefkSNzL7GhkyyWKEoOKmCiCFy4joAcVUBgZYIaZ\n6bl0d3X9zh9PPd3VTc901+15unver7VqVfVTz1P16/pBz6d+10gpIUmSpOWhUnYBJEmStHiGN0mS\npGXE8CZJkrSMGN4kSZKWEcObJEnSMmJ4kyRJWkYMb5IkScuI4U2SJGkZMbxJkiQtI4Y3SZKkZcTw\nJkmStIwY3iRJkpYRw5skSdIy0ld2AbohIg4DXgP8ApgouTiSJEkHMwAcC3wzpbSz1YtXRHgjC243\nlF0ISZKkFrwZ+OdWL1op4e0XANdffz0nnHBC2WWRJEk6oAceeIC3vOUt0MgvrVop4W0C4IQTTuDk\nk08uuyySJEmL0dZQLycsSJIkLSOGN0mSpGXE8CZJkrSMGN4kSZKWkZUyYUGSpENerVZjx44djI2N\nkVIquziHlIhgcHCQtWvXMjw8TET07L0Mb5IkrQApJX75y1+yb98+qtUqfX3+E1+kqakpdu7cyc6d\nO1m3bh2jo6M9C3DWrCRJK8Du3bvZt28fhx12GEcddVRPW340v4mJCbZu3cr27dsZHh5mZGSkJ+/j\nmDdJklaAXbt2AfS0xUcHNzAwwFFHHQXM1EcvGN4kSVoBJicn6evrs7u0ZAMDA/T39zM+Pt6z9zC8\nSZK0AqSUqFT8Z30piIieThixliVJWiHsLl0ael0PhjdJkqRlxPC2SH90zV287s9v4ff/8QdlF0WS\npEPOt7/9bS655BKeeuqprr/2+eefz8aNG7v+ur1ieFukX+3az8+e2MMvduwruyiSJB1yvv3tb3Pp\npZf2JLxddNFFXHfddV1/3V5xSsoijQxmH9We8VrJJZEkSQezb98+Vq9evejzN23a1MPSdJ8tb4s0\n3Ahvew1vkiQV6pJLLuGP/uiPADj++OOJCCKCW265hY0bN3LWWWdx7bXX8qIXvYhVq1Zx6aWXAvDx\nj3+cV7/61YyOjjI8PMzmzZu58sormZycnPX683WbRgS///u/z2c+8xme97znMTQ0xK/92q/xpS99\nqZDf+WBseVukvOVtzPAmSVKh3v3ud7N9+3Y+9rGPce21104vhPv85z8fgB/84Afcd999fOQjH+H4\n449neHgYgJ/+9Kece+65HH/88QwMDHDXXXdx+eWXc//993P11Vcv+L433ngjd9xxB5dddhkjIyNc\neeWVnH322fz4xz/mOc95Tu9+4QUY3hZpaKAKwJ6JKVJKTseWJC0Ll37xR9z7aO9W+2/V849ey8W/\neXJL1zzrWc/i2c9+NgAvetGLntZKtm3bNu69916e+9znzjr+l3/5l9OP6/U6r3rVq1i/fj3vete7\n+Iu/+AsOP/zwg77vvn37uPnmm1mzZg0Ap556KkcffTSf//znufDCC1v6HbrJ8LZIebfpVD0xXquz\nqr9acokkSVrYvY/u4rs/3152MXrqBS94wdOCG8APf/hDLr74Yv793/+d7dtnfwY/+clPOP300w/6\nuq997WungxvAkUceyejoKA899FB3Ct4mw9si5d2mkE1aMLxJkpaD5x+9tuwizNKL8uTdqM0efvhh\nXvWqV3HiiSdy1VVXsXHjRlatWsXtt9/Oe9/7XvbtW3j1iPXr1z/t2ODg4KKu7SXD2yINzwpvU6wf\nKbEwkiQtUqtdlMvRfEOZrr/+evbs2cO1117LcccdN338zjvvLLJoPdHWbNOIeF1EXB0R90fEnoh4\nJCJuiIgXL+La8yMiHeD2zHbKU4SRwZmWNictSJJUrMHBQYBFt3rlgS6/DrL9X//u7/6u+4UrWLst\nb+8B1gNXAfcCG4APALdFxG+klL6+iNd4F3D/nGNPtlmenhsaaGp5mzC8SZJUpM2bNwNw1VVXcd55\n59Hf38+JJ554wPPf8IY3MDAwwDnnnMMHP/hB9u/fzyc/+Ul27NhRVJF7pt113t6bUnpdSumTKaVv\nppT+B/AGsvD14UW+xpaU0m1zbpMLX1aO4Tlj3iRJUnHOOOMMPvShD/HFL36RV77ylZx22ml8//vf\nP+D5J510El/4whfYsWMHb33rW3nf+97HC1/4Qv7mb/6mwFL3RlstbymlbfMcG4uIe4FjOy7VEjQy\nZ8ybJEkq1hVXXMEVV1wx69iDDz54wPPPOusszjrrrKcdTynN+vnTn/70gucs5v2K0rUJCxFxGHAq\nsJguU4AvRcQGYCdwC/CfU0pbFvE+o2TdtM16vq/FcNOYN1veJElSWbo52/TjwDBw+QLn/apxzm3A\nLmAzcCHZeLlXpJTuWuD6C4CLOyxry4abxrw5YUGSJJWlK+EtIj4K/A7wvpTSgTuggZTSTcBNTYdu\njYgbgXuAy4A3L/B2nwCumXNsE3BDS4VuUfOYt71OWJAkSSXpOLxFxMXAR4A/Tin9bTuvkVJ6MCL+\nDXjZIs7dBswac1fEVlUDfRUGqhUmpuqMOeZNkiSVpN3ZpsB0cLsEuCSldMUCpy/4ckC9w9foqXzc\nm2PeJElL0YEG2atYva6HtsNbRFxEFtz+JKV0aSeFiIjjgVeQjYNbsvK13gxvkqSlJiKo15d0G8gh\nI6XU017BtrpNI+IDZOPTbgJujIhZ3Z0ppdsa530KOA/YlFJ6qHHsZuBW4G5mJix8EEjARe39GsXI\nlwtxkV5J0lLT39/P/v37qdVq9PW5+2VZJiYmmJycZGhoqGfv0W7t/mbj/o2N21x53Kw2bs3x8x7g\n7cAfAqvJxq99HfhoSuknbZanEDPdpo55kyQtLWvXrmX37t1s27aNo446qpDx4JptYmKCrVu3All9\n9Eq7i/SescjzzgfOn3PsD9p5z6Ugn3HqUiGSpKVmzZo1DA0NsXPnTsbGxqhWqwa4gqSUSCkxOZlt\nFLVu3TqGh4d79n62q7Zg2DFvkqQlKiI45phj2LFjB2NjY05eKFBEUKlUGBoaYu3atQwPDy+9MW+H\nqrzlbe+E3aaSpKWnr6+PDRs2sGHD3I2ItJJ0tFTIoWakMebNblNJklQWw1sL8pa3PeM1m6MlSVIp\nDG8tyMNbrZ4Yr7mWjiRJKp7hrQXDA9Xpx457kyRJZTC8taB5c3pnnEqSpDIY3low0hTenLQgSZLK\nYHhrwZAtb5IkqWSGtxbkS4UA7HHMmyRJKoHhrQWOeZMkSWUzvLUg3x4LHPMmSZLKYXhrgS1vkiSp\nbIa3FgwPus6bJEkql+GtBYN9VfqrAdhtKkmSymF4a1Hz/qaSJElFM7y1KJ+0YMubJEkqg+GtRfm4\nt73jjnmTJEnFM7y1aLrbdMKWN0mSVDzDW4vy/U3tNpUkSWUwvLVoaCDrNnXCgiRJKoPhrUUzs00d\n8yZJkopneGvRiGPeJElSiQxvLXKdN0mSVCbDW4uGG2PeJqcS4zW7TiVJUrEMby1q3pzetd4kSVLR\nDG8tag5vLhciSZKKZnhr0UhTeHPSgiRJKprhrUX5Om/gpAVJklQ8w1uLZrW8OeZNkiQVzPDWouFZ\n4c2WN0mSVCzDW4tGnLAgSZJKZHhrkWPeJElSmQxvLZrVbTrhmDdJklQsw1uLBvsq9FUCsOVNkiQV\nz/DWoohwf1NJklQaw1sb8v1Nx1wqRJIkFczw1gZb3iRJUlkMb22YDm9ujyVJkgpmeGvDiC1vkiSp\nJIa3NuRrvbk9liRJKprhrQ15y5s7LEiSpKIZ3tqQj3nb65g3SZJUMMNbG2Zmm9ptKkmSimV4a8PI\nYDbmbWKqzkStXnJpJEnSocTw1oahgab9TR33JkmSCmR4a8PIrM3pDW+SJKk4hrc2DDeHN8e9SZKk\nAhne2jDcGPMGLhciSZKKZXhrw+yWN8ObJEkqjuGtDcNNExZc602SJBXJ8NaG5gkLY455kyRJBTK8\ntaF5zJvdppIkqUhthbeIeF1EXB0R90fEnoh4JCJuiIgXL/L60Yj4dEQ8ERF7I+I7EfH6dspShuFZ\nLW+GN0mSVJx2W97eA2wErgLeBLwfGAVui4jXHezCiBgEvga8vnHdm4HHgJsi4jVtlqdQg30VqpUA\nHPMmSZKK1bfwKfN6b0ppW/OBiLgJeAD4MPD1g1z7u8ApwMtTSt9pXPsN4C7gSuD0NstUmIhgeKDK\nrv0113mTJEmFaqvlbW5waxwbA+4Fjl3g8rOBH+fBrXFtDfgH4KURcUw7ZSpaPmnBblNJklSkdlve\nniYiDgNO5eCtbpC1un1rnuN3N+5PBh45yPuMAhvmHN60yGJ2zVAjvDlhQZIkFalr4Q34ODAMXL7A\neeuB7fMc3970/MFcAFzcWtG6L5+0sGfCblNJklScroS3iPgo8DvA+1JK31/EJanN5wA+AVwz59gm\n4IZFvG/XjDSWC7HlTZIkFanj8BYRFwMfAf44pfS3i7jkSeZvXVvXuJ+vVW5aY7zd3MkSi3jb7sp3\nWTC8SZKkInW0SG8juF0CXJJSumKRl90DbJ7neH5sSydlKsqwExYkSVIJ2g5vEXERWXD7k5TSpS1c\neh1wUkRMLwkSEX3AO4HvppQebbdMRcp3WdjrmDdJklSgdndY+ABwGXATcGNEvKz51nTepyKiFhHH\nNV1+NfAj4JqIODcifh34PHAi8J/a/k0KZsubJEkqQ7tj3n6zcf/Gxm2ufBBatXGbHpSWUhpvbIV1\nJfAxYAi4EzgzpfTNNstTuJHGmLeJWp3JqTr9VbeJlSRJvddWeEspnbHI884Hzp/n+GPAee2891Ix\n1LS/6Z7xGs8YGiixNJIk6VBhc1Gb8qVCwLXeJElScQxvbRqe0/ImSZJUBMNbm5rDm5MWJElSUQxv\nbcoX6QVb3iRJUnEMb20abh7zNu6YN0mSVAzDW5tGHPMmSZJKYHhr06wJCxOGN0mSVAzDW5uax7w5\nYUGSJBXF8NamVf0VKo19I/Y65k2SJBXE8NamiHB/U0mSVDjDWwfySQtOWJAkSUUxvHVgaCBbLsQJ\nC5IkqSiGtw7MtLw55k2SJBXD8NaBYbtNJUlSwQxvHXDCgiRJKprhrQPDjnmTJEkFM7x1IG95c503\nSZJUFMNbB0bsNpUkSQUzvHUgb3kbr9WpTdVLLo0kSToUGN46kK/zBi4XIkmSimF460DebQpOWpAk\nScUwvHVguDm8Oe5NkiQVwPDWgeaWNyctSJKkIhjeOuCYN0mSVDTDWweGHfMmSZIKZnjrwIhj3iRJ\nUsEMbx1wwoIkSSqa4a0Dw4MzY97GHPMmSZIKYHjrwOr+KpXIHu91zJskSSqA4a0DEcHwgPubSpKk\n4hjeOpSPe3PMmyRJKoLhrUP5uDfXeZMkSUUwvHVouuXNMW+SJKkAhrcO5WPe7DaVJElFMLx1KG95\nc6kQSZJUBMNbh0amx7zZ8iZJknrP8NahoUbLm+u8SZKkIhjeOjQy6DpvkiSpOIa3DuUTFvZP1qlN\n1UsujSRJWukMbx1q3t90z4STFiRJUm8Z3jqUzzYFx71JkqTeM7x1qDm8OeNUkiT1muGtQyNN3aau\n9SZJknrN8NahfMIC2PImSZJ6z/DWoeZuU5cLkSRJvWZ465ATFiRJUpEMbx0adsybJEkqkOGtQyPO\nNpUkSQUyvHVodX+ViOyx4U2SJPWa4a1DETE943SP3aaSJKnHDG9dkI97s+VNkiT1muGtC/IZp2PO\nNpUkST1meOuCmW5Tw5skSeqttsNbRKyJiCsj4qsR8XhEpIi4ZJHXnt84f77bM9stU1nybtO9jnmT\nJEk91rfwKQe0Hvg94C7geuDdbbzGu4D75xx7soMylSJfLsQdFiRJUq91Et4eAg5PKaWIOIL2wtuW\nlNL3OijDkpCPedvjmDdJktRjbYe3lFLqZkGWsyHHvEmSpIJ00vLWDV+KiA3ATuAW4D+nlLYc7IKI\nGAU2zDm8qTfFW5yR6aVCHPMmSZJ6q6zw9ivgcuA2YBewGbgQuC0iXpFSuusg114AXNz7Ii5e3m26\nb3KKqXqiWomSSyRJklaqUsJbSukm4KamQ7dGxI3APcBlwJsPcvkngGvmHNsE3NDVQrZg1v6mEzXW\nruovqyiSJGmFK7vbdFpK6cGI+DfgZQuctw3Y1nwsotyWrnzMG2Tj3gxvkiSpV5baIr0B1MsuRKvy\ndd7AcW+SJKm3lkx4i4jjgVeQjYNbVmZ1mzrjVJIk9VBH3aYRcSYwDKxpHHp+RPx24/GXU0p7I+JT\nwHnAppTSQ43rbgZuBe5mZsLCB4EEXNRJmcowbHiTJEkF6XTM2yeB45p+flvjBnA88CBQbdyaB6bd\nA7wd+ENgNdkYtq8DH00p/aTDMhVuuGnMm7ssSJKkXuoovKWUNi7inPOB8+cc+4NO3nepaR7ztnfC\nMW+SJKl3lsyYt+WsecybLW+SJKmXDG9d4Jg3SZJUFMNbF6zub14qxPAmSZJ6x/DWBZVKMDzQ2N/U\nMW+SJKmHDG9dkned2vImSZJ6yfDWJfmkBScsSJKkXjK8dclQY7kQW94kSVIvGd66JF+o1zFvkiSp\nlwxvXTLimDdJklQAw1uXOGFBkiQVwfDWJfkWWWPjdptKkqTeMbx1ST7mbe+ELW+SJKl3DG9dkneb\n7p2Yol5PJZdGkiStVIa3LmnenH6PrW+SJKlHDG9dkq/zBrDHcW+SJKlHDG9dYsubJEkqguGtS/IJ\nC+ByIZIkqXcMb10y3NTy5v6mkiSpVwxvXTLsmDdJklQAw1uXNLe8udabJEnqFcNbl4zYbSpJkgpg\neOuS5pY3JyxIkqReMbx1yVD/zJg39zeVJEm9YnjrkkolGBrIAtxeW94kSVKPGN66KO86dZFeSZLU\nK4a3LsonLdhtKkmSesXw1kX5Wm9OWJAkSb1ieOuiocYWWYY3SZLUK4a3LhpxzJskSeoxw1sXTU9Y\ncMybJEnqEcNbF400xry5w4IkSeoVw1sX5WPeXOdNkiT1iuGti2bWeZuiXk8ll0aSJK1EhrcuyrtN\nAfZOOu5NkiR1n+Gti9ycXpIk9ZrhrYuGBwxvkiSptwxvXTS75c1uU0mS1H2Gty4abhrz5nIhkiSp\nFwxvXTTimDdJktRjhrcuGmoe8+YWWZIkqQcMb1004pg3SZLUY4a3Lmoe82a3qSRJ6gXDWxc1d5s6\nYUGSJPWC4a2LqpVgdX/W+rbXMW+SJKkHDG9dlq/1NuaYN0mS1AOGty7L9zd1zJskSeoFw1uX5S1v\nhjdJktQLhrcuy/c3dZ03SZLUC4a3Lhue7jZ1zJskSeo+w1uX2W0qSZJ6yfDWZSPTs00Nb5IkqfsM\nb12WL9S7d8JuU0mS1H2Gty6bXipkokZKqeTSSJKklabt8BYRayLiyoj4akQ8HhEpIi5p4frRiPh0\nRDwREXsj4jsR8fp2y7NU5GPeUrL1TZIkdV8nLW/rgd8DBoHrW7kwIgaBrwGvB94PvBl4DLgpIl7T\nQZlKl4c3cNKCJEnqvr6FTzmgh4DDU0opIo4A3t3Ctb8LnAK8PKX0HYCI+AZwF3AlcHoH5SrVmlUz\nH+nOfZOMrl1VYmkkSdJK03bLW2po8/KzgR/nwa3xejXgH4CXRsQx7ZarbKNrZsLatt3jJZZEkiSt\nRJ20vHXiFOBb8xy/u3F/MvDIfBdGxCiwYc7hTd0rWmdG1w5OP962e3+JJZEkSStRWeFtPbB9nuPb\nm54/kAuAi7teoi4ZXTMT3h7bZcubJEnqrrLCG8DBulwP9twngGvmHNsE3NBxibpgZLCP1f1V9k1O\nsc3wJkmSuqys8PYk87eurWvcz9cqB0BKaRuwrflYRHSvZB2KCEbXDvLQk3vtNpUkSV1X1iK99wCb\n5zmeH9tSYFm67sjGpAUnLEiSpG4rK7xdB5wUEdNLgkREH/BO4LsppUdLKldXbGhMWnjc8CZJkrqs\no27TiDgTGAbWNA49PyJ+u/H4yymlvRHxKeA8YFNK6aHGc1cD7wWuiYgLybpBLwBOBH69kzItBfmk\nhW277DaVJEnd1emYt08CxzX9/LbGDeB44EGg2rhND0xLKY03tsK6EvgYMATcCZyZUvpmh2UqXb7W\n256JKcbGa4wMljkvRJIkrSQdpYqU0sZFnHM+cP48xx8ja5FbcZqXC9m2az8jG0ZKLI0kSVpJyhrz\ntqLNXqjXcW+SJKl7DG894BZZkiSpVwxvPXDk2tndppIkSd1ieOuBw1b3M9CXfbQuFyJJkrrJ8NYD\nEcGGkcZyIYY3SZLURYa3HsknLTxmt6kkSeoiw1uPTC/Ua8ubJEnqIsNbj+QzTp2wIEmSusnw1iN5\ny9uu/TX2T06VXBpJkrRSGN565Mi1M2u9OeNUkiR1i+GtRzbM2mXBrlNJktQdhrcemb2/qS1vkiSp\nOwxvPdK8RZbLhUiSpG4xvPXI+uEBqpUAXC5EkiR1j+GtRyqV4IiRAcDwJkmSusfw1kP5jFPDmyRJ\n6hbDWw9N77LgmDdJktQlhrce2tCYtOA6b5IkqVsMbz2Ut7w9uWeCiVq95NJIkqSVwPDWQ6NNC/U+\nMWbrmyRJ6pzhrYea13pz0oIkSeoGw1sPzd5lwUkLkiSpc4a3HmrenN6WN0mS1A2Gtx46YmSAyDZZ\nMLxJkqSuMLz1UF+1wvrhbJeFx3fbbSpJkjpneOuxfK23x3bZ8iZJkjpneOux6V0WbHmTJEldYHjr\nsZktsmx5kyRJnTO89Vg+4/SJsXGm6qnk0kiSpOXO8NZj+S4L9QRP7rH1TZIkdcbw1mOzF+o1vEmS\npM4Y3npsw6wtspy0IEmSOmN46zFb3iRJUjcZ3npsQ3N4c5cFSZLUIcNbj63qr3LY6n7AblNJktQ5\nw1sBjlzrWm+SJKk7DG8FGG1MWrDbVJIkdcrwVoB80sLjhjdJktQhw1sBNqyd2d80JXdZkCRJ7TO8\nFSDvNp2cSuzYO1lyaSRJ0nJmeCvArLXenHEqSZI6YHgrQL45PTjjVJIkdcbwVoBRF+qVJEldYngr\nwOhau00lSVJ3GN4KMDTQx8hgH2C3qSRJ6ozhrSB516ktb5IkqROGt4LkG9Tb8iZJkjpheCvI6Fq3\nyJIkSZ0zvBXkyDXusiBJkjpneCtIPuN0/2Sd3eO1kksjSZKWK8NbQfItssBxb5IkqX2Gt4LMWqh3\nlzNOJUlSewxvBZm9UK8tb5IkqT1th7eIGImIv46IRyNif0TcGRHvWMR150dEOsDtme2WZ6nb0Nxt\n6lpvkiSpTX0dXHstcBpwIfAT4FzgsxFRSSn94yKufxdw/5xjT3ZQniVt7ao+BvsqjNfqjnmTJElt\nayu8RcSbgDcA56aUPts4/I2IOA74s4j4p5TS1AIvsyWl9L123n85igiOXLuKh7fvtdtUkiS1rd1u\n07OBMeCaOcf/HjgaOL2TQq1UbpElSZI61W54OwW4L6U0d8Gyu5ueX8iXImIqIrZHxLURsZhriIjR\niDi5+QZsaqHspcknLdhtKkmS2tXumLf1wM/mOb696fkD+RVwOXAbsAvYTDZu7raIeEVK6a4F3vsC\n4OLWirs05Gu92W0qSZLa1cmEhYPt8XTA51JKNwE3NR26NSJuBO4BLgPevMD7foKnd9duAm5Y4LrS\n5ZvTj43X2DtRY2igk49fkiQditpND08yf+vausb99nmeO6CU0oMR8W/AyxZx7jZgW/OxiGjl7Uoz\ne6HecTYeYXiTJEmtaXfM2z3A8yJibvrY3Ljf0sZrBlBvszzLwpFrm9d6s+tUkiS1rt3wdh0wAvzW\nnOPnAY8C323lxSLieOAVZOPgVqzZuyw441SSJLWurX67lNJXIuJfgU9GxFrgAeAc4I3AO/M13iLi\nU2SBblNK6aHGsZuBW8lmpuYTFj5INk7uos5+naXNzeklSVKnOhl09VayWaOXkY11ux84J6X0uaZz\nqo1b86C0e4C3A38IrCYbv/Z14KMppZ90UJ4l7/ChfvqrweRU4jFb3iRJUhvaDm8ppTHg/Y3bgc45\nHzh/zrE/aPc9l7uIYMPIII/u3M/jtrxJkqQ2tL0xvdqzYa1rvUmSpPYZ3grmFlmSJKkThreCHZlv\nkWXLmyRJaoPhrWD5jNOn9k4yXpsquTSSJGm5MbwVbO4uC5IkSa0wvBVs9kK9hjdJktQaw1vBmhfq\nfdxJC5IkqUWGt4LN6ja15U2SJLXI8Faw9SODVBr7TTjmTZIktcrwVrBqJThixLXeJElSewxvJRh1\nrTdJktQmw1sJ8kkLj9ltKkmSWmR4K0E+acHZppIkqVWGtxLk4e3JPRPUpuoll0aSJC0nhrcSbFib\ndZumBE+MTZRcGkmStJwY3kpw5Ky13uw6lSRJi2d4K8Ho2pldFlzrTZIktcLwVoLmXRYes+VNkiS1\nwPBWgnyRXrDlTZIktcbwVoKBvgrrhgcAF+qVJEmtMbyVxLXeJElSOwxvJcknLdjyJkmSWmF4K0ne\n8uaYN0mS1ArDW0mmu03Hxpmqp5JLI0mSlgvDW0ny8DZVT2zfs/AuC3UDniRJAvrKLsChatZCvbv3\ns6Fp7beUEj97Yg/fe3A7dzy4g+89uJ1f7tjHa567gY++5RSOfsbqMoosSZKWAMNbSZoX6n1kxz7G\na/XpsPb9h3bM2xr3tfu38d2/upWP/C/P4+2nHUtEFFlkSZK0BBjeSjK6Zqbl7fc+8/0DnrdmsI9T\njzuc1f1VbvrRrxgbr3Hhtfdw4z1b+dPfegHH2AonSdIhxfBWktG1g/RVgtqcsWxHH7aKl2xcx2kb\nD+fFx63jxGeuoVrJWti+cf82PnTtPfxq136+9f89wW/81a18+E3P45yX2gonSdKhwvBWklX9VT70\npudx05atnPTMtbxk4+G8ZOO6g7akvfakUf7lD17N5Tfey+e/90vGxmt8+Lp7+PI9W/kvb93MseuG\nCvwNJElSGSKl5T+LMSJOBrZs2bKFk08+ueziFOKWH2etcFt3Zjs0DA9UufBNz+N3XvpsKhVb4SRJ\nWqp+9KPRxoEYAAAQXUlEQVQfccoppwCcklL6UavX2/K2TJ1xYtYKd8WN9/G5O37BnokpLrp+C1++\neyvvfe0JjNem2L2/xu79k+zaX2PX/snGz9mx3ftrVCvBG09+Jme/6BgOb+y1KkmSljZb3laAW3/y\nOBd+4W4e3dnePqkD1Qr/4eQjecdpz+blm9bbcidJUg/Z8iZe/dwNWSvcl+/ns7c/PO85/dVg7ap+\n1qzqY03j/pGn9vHQk3uZmKrzpbu38qW7t/Ksw1fzH19yLG97ybM46jBnskqStNTY8rbC/PTxMR7Z\nsY+1q/Og1sfaVf0M9lWeNiO1Xk989+fb+fz3fsGX79nKeK0+/VwlslD4jtOO5XUnHclAn5txSJLU\nDZ22vBneBMDOvZPccNcjfO72X3Dv1l2znjtiZIDXnTTKaRvXcfrx6zl23WqXJpEkqU2GNwxv3bbl\nkZ187o6HueHOR9m9v/a0549cO9gIcus47fh1PHd0jePkJElaJMMbhrde2TcxxVe2bOWGOx/l+w/t\nYGz86UEO4LDV/Zy28XBO27iOk48+jA1rBjliZIDDhwYMdZIkzeGEBfXM6oEqbz31Wbz11GdRm6pz\n39bd3P7gdu74+XZuf3D79P6rO/dNcvN927j5vm2zrq9WgnXDA2wYGeSIRqDbMDLIESODHLEmC3fr\nhmfuhwaqdsdKkrQAw5sWpa9aYfOzDmPzsw7jd195PCklfvr4Hm7/+XbueHA7t/98O488tW/WNVP1\nxOO7x3l89zhsXfg9BvoqHD7UPxPqhgdYNzTA2tV9jAz2M7KqjzWDfYwM9jGyKrvPZ8+ODPY5qUKS\ndEgwvKktEcEJoyOcMDrCuac/G4BHntrHw0/u5YmxcZ4Yy0Jb9ngiu9+dPZ6Yqs/7mhO1Oo/tGuex\nXeNtlWmgr5KFu6ZgNzLY37hv/Nx4PFCt0F+t0N9XYaBaYaAv6K9mj/Nj/dUKg30VBvsrDPZVs8d9\nFfqqhkRJUnkMb+qaY56x+qB7swKklNi1r8YTe8Z5au8E2/dMsmPPBNv3TmT3eybYsXeCHXtnju/e\nX2OqvvDYzIlanSdrEzzZ6M7tlWolpoPcYF+Vwf4s6FUCKhFExPTjSjDn56C/LxqBscJAX3X6cf6a\nA43w2Mp4wUrTe0TTe1cqWXmicU5fJahWgr5qHODnyvTPfZUs0GaPK/RXg75qhf5Kdt9XDforFSKY\nfs/8faLxe88npURKMJUS9cbjekrUU9ZaO6v8s36fmc9Tkg5lhjcVKiI4bKifw4b6F31NSon9k3V2\nj2fbeo3trzE2nm31ld1nx/eM19g93vz85PQ5+fmLCYELmaon9k5MsXdiCpjs+PVWsuYAVm+EtG7M\nkcpft1qZCaD91crMz9WmIFqZE6grBw7X9ZSoTSUm64naVL3xOLuvTdWnj0/VE5VKUG0K69VKI2BW\nZofN/PF0CK3MDvl54J3vnNkhduZxtRr0T4frRrCeDtgzjyOCfFJaSpB/9HkdpMaRILsu/7wqMfvn\naqWSHa9kAb3bqo06rDbqNP9sKxUa9dj8eTz985n7BSkaX1wO9gVg7nPQ2peQIqSUmKonqo3/hqWc\n4U1LXkSweqDK6oEqo2vaf52UEuO1OmPjNSZqdSansttELTU9rjMxVWdyKjUeTzE+WWe8Vme8Nudx\nrd74eYrJqUQiUa/PtCKllBqtS9njeuMPcf7a47Wp7D0a7zne9HgFTAIHZgJbr163Vk+018kuLd7c\nUEfQCN4zIZCmMBjMBESYOdb8PGStz1P1ObemY83fNSvBrC8k1WpTy3njeDUP101laH5fmso1/Tsd\n4EvErMBbCapNX1CaA/bMsezzyK/N3zuaPqc8gKa8pT2l7HHT3816429lPdF4z+yLQ94b0Fed+bla\nyXoCKpW5X3Rm9z40/17N5v5pSsw+8LYXH7tkx1Ib3nTIiAhW9VdZ1V8tuygHlRqhZLGhJz+t+Y9f\nqs/+I9j8x3JqKlGr16f/oahNZf9Q1Or5ffZcbSoLtbV6475x3WSjFarWCKKJrDUtTb9X3sKW/SnM\ny1AJmlqq5v5hnf0PTHNXav5a9TQnGOf/0E01lz0xVa/P+rk2VZ/1GUy/bj3N+ozq9TTd4jTTPTy7\nq7i/qUWvOZznrzVVbwrqjePzhfrZv1v2j8ZUPf8c56+75nLO1xo4q5WwCy3MmpF38zd+KqUM9UQ2\nXniqlLc/JL35hccY3iQtTkTQX7WLRO1LKQvWuelWF2ZaP6LpuXy8YXN4n6rPDfVZMF7c+/O0Vo6D\nnTu3BSoPws2tUTNh+8CBvjlAL/YLQGImMOef3awvIcx+vfwLCU3hu/k98tfLv9RkOXp2MM9rptro\nAs9bsKa7rZu6r6uVmG61n66LqZkvKXlYr9VnvqQ03rLpi1Xz70JTOZ/+JSL/otH8JSL/IjJTNzN1\nMtVoOZuqN32Rm+d9EvmXmaYhBvN1Z1dmvuTlwxjy/yYnp/Lei/r053GoMrxJ0goTEQz0Lf4LQN4l\n1vipN4WSuiw1hVp4eu9DPimqOYzOHbU590tG849DS7iXxvAmSZKWnZge5lB2SYq3NDtzJUmSNC/D\nmyRJ0jJieJMkSVpGDG+SJEnLiOFNkiRpGWk7vEXESET8dUQ8GhH7I+LOiHjHIq8djYhPR8QTEbE3\nIr4TEa9vtyySJEmHik5a3q4FzgMuBc4E7gA+GxHnHuyiiBgEvga8Hng/8GbgMeCmiHhNB+WRJEla\n8dpa5y0i3gS8ATg3pfTZxuFvRMRxwJ9FxD+llA60icfvAqcAL08pfafxet8A7gKuBE5vp0ySJEmH\ngnZb3s4GxoBr5hz/e+BoDh7AzgZ+nAc3gJRSDfgH4KURcUybZZIkSVrx2g1vpwD3NUJXs7ubnj/Y\ntXfPczw/dnKbZZIkSVrx2t0eaz3ws3mOb296/mDXbp/n+GKuJSJGgQ1zDm862DWSJEkrRSd7m6Y2\nn+v02guAixc4R5IkaUVqN7w9yfwtZOsa9/O1rHXjWoBP8PSxdicB/+OBBx5Y4FJJkqRyNeWVgXau\nbze83QOcExF9c8a9bW7cb1ng2s3zHF/MtaSUtgHbmo9FxCaAt7zlLQe7VJIkaSk5FvhhqxdFSgv1\nUs5zUcSZwJeBd6SU/qnp+FeAFwDPPtBSIRHxHrLWs5ellL7bONYH3AmMpZRe1kZ5DgNeA/wCmGj1\n+hZsAm4gW5vupz18H7XOulnarJ+ly7pZ2qyfpauTuhkgC27fTCntbPWN22p5Syl9JSL+FfhkRKwF\nHgDOAd4IvDMPbhHxKbKFfDellB5qXH418F7gmoi4kKwV7QLgRODX2yzPTuCf27m2FRGRP/xpSulH\nvX4/LZ51s7RZP0uXdbO0WT9LVxfqpuUWt1wnExbeClwOXEY2Xu1+4JyU0ueazqk2btO/YUppvLEV\n1pXAx4Ahsla3M1NK3+ygPJIkSSte2+EtpTRGtr3V+w9yzvnA+fMcf4ysRU6SJEkt6GRvU0mSJBXM\n8Naax4FLG/daWqybpc36Wbqsm6XN+lm6SqubtmabSpIkqRy2vEmSJC0jhjdJkqRlxPAmSZK0jBje\nJEmSlhHDmyRJ0jJieFtARIxExF9HxKMRsT8i7oyId5RdrkNNRKyJiCsj4qsR8XhEpIi45ADnnhoR\nN0fEWEQ8FRHXRsRzCi7yISMiXhcRV0fE/RGxJyIeiYgbIuLF85xr3RQsIl4YETdGxMMRsS8itkfE\ndyLinfOca/2ULCLe3fj7NjbPc9ZPgSLijEZdzHd72Zxzf73x/9XeiHgiIj4dEaO9KpvhbWHXku0G\ncSlwJnAH8NmIOLfUUh161gO/BwwC1x/opIg4CbiFbNPf/wj8b8BzgW9FxIbeF/OQ9B5gI3AV8Cay\nXVdGgdsi4nX5SdZNaZ4B/AL4MFn9/K/Ag8BnIuIj+UnWT/ki4hjgz4FH53nO+inPh4H/ec5tS/5k\nRLwG+ArwGNkm9e8n26v9axEx2JMSpZS8HeBG9ocuke3Z2nz8q8AjQLXsMh4qN7L9cfN1CY9o1Msl\n85z3ebIFE9c2HTsOmAD+a9m/x0q8AaPzHBsBfgXcbN0szRtwG/Cw9bN0bsAXgX8GPg2MzXnO+im+\nPs5o/Fvz2wucdzvwI6Cv6djLG9e+pxdls+Xt4M4GxoBr5hz/e+Bo4PTCS3SISg0HOyci+oCzgC+k\nlHY1XfsQ8A2y+lSXpZS2zXNsDLgXOBasmyXqCaAG1s9S0OjGfg1wwTzPWT9LVKO19DTgMymlWn48\npfRt4Cf0qG4Mbwd3CnBfc4U03N30vJaOTcBqZuqn2d3ACRGxqtgiHZoi4jDgVLJvo2DdlC4iKhHR\nFxEbIuIC4DeA/9p42vopUWNs1F8DF6aUfjnPKdZPuT4eEbWI2BUR/xIRr2x6Ls8BB6qbnuQEw9vB\nrQe2z3N8e9PzWjry+jhQnQVweHHFOaR9HBgGLm/8bN2U7xPAJLAN+Cvg/0op/d+N56yfcn0C+DHw\nyQM8b/2UYyfZWN7/A3gt2Vi2Y4FbIuI3GucsVDc9yQl9vXjRFeZgXXVuDLs0WWclioiPAr8DvC+l\n9P05T1s35bkC+H/JJpP8JvC3ETGcUvrzpnOsn4JFxG+R1ceLFhoagvVTqJTSD4EfNh36VkRcB9wD\nXAn8S/PpB3qZXpTN8HZwTzJ/al7XuJ8vaas8TzbuD1RnCXiquOIceiLiYuAjwB+nlP626SnrpmQp\npYeBhxs/fjkiAP5LRPw3rJ9SRMQIWSv1x4BHI+IZjacGGs8/g6y11PpZIlJKT0XEl4D/MyJWs3Dd\n9CQn2G16cPcAz2sMFm22uXG/BS0lPwX2MVM/zTYDD6SU9hdbpENHI7hdQjYL+Io5T1s3S8/tZF/g\nn4P1U5YjgCOBDwA7mm7nkA072AH8d6yfpSYa94mZHHCguulJTjC8Hdx1ZEse/Nac4+eRrcPz3cJL\npANqTCz5IvDWiFiTH4+IZ5ONV7i2rLKtdBFxEVlw+5OU0qVzn7dulqTXAnXgZ9ZPaX5F9vnOvf0L\nsL/x+CPWz9IREYeTzfy9M6W0P6X0CNkXoXdGRLXpvJcBJ9KjuomFu9gPbRHxVeAlwH8CHiD7RvS/\nA+9MKf33Mst2qImIM8m+ja4BriZbwuXzjae/nFLa21jI8g7gB8CfAquAy8iar1+YUnq88IKvcBHx\nAbKFRW8iW8x6lpTSbY3zrJsSRMT/A+wi+wfmMbLWnrcBbwf+LKX0wcZ51s8SERGfJltbbKTpmPVT\nsIj4R7KhBt8jW1rnfyJrJd0EnJlSurlx3hnAv5IF7E+QjSv9U7IJDy9JKY13vXBlL4K31G9kLW9X\nAVuBceAu4B1ll+tQvJGtCp8OcNvYdN6LgZuBPY3/ea4DNpVd/pV6I1v1/UD1kuaca90UXz/vAm4l\nW+B1kqwr7hayL6Bzz7V+lsCNeRbptX5KqYcLySYsPEW2JuI2spa00+Y59w3Ad8i6t58E/hvzLGDe\nrZstb5IkScuIY94kSZKWEcObJEnSMmJ4kyRJWkYMb5IkScuI4U2SJGkZMbxJkiQtI4Y3SZKkZcTw\nJkmStIwY3iRJkpYRw5skSdIyYniTJElaRgxvkiRJy4jhTZIkaRn5/wFVughKbDaarQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1098740b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,\n",
    "          weight_decay):\n",
    "    net = get_net()\n",
    "    train(net, X_train, y_train, None, None, epochs, verbose_epoch, \n",
    "          learning_rate, weight_decay)\n",
    "    preds = net(X_test).asnumpy()\n",
    "    test['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test['Id'], test['SalePrice']], axis=1)\n",
    "    submission.to_csv('submission.csv', index=False)\n",
    "    \n",
    "learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate, weight_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
